OMG UR Funny!

Computer-Aided Humor with an Application to Chat

Miaomiao Wen

Carnegie Mellon

University

Nancy Baym, Omer Tamuz, Jaime Teevan, Susan Dumais, Adam Kalai

Microsoft Research

Abstract

In this paper we explore Computer-Aided Humor (CAH), where a computer and a human collaborate to be humorous. CAH systems support people’s natural desire to be funny by helping them express their own idiosyncratic sense of humor. Artificial intelligence research has tried for years to create systems that are funny, but found the problem to be extremely hard. We show that by combining the strengths of a computer and a human, CAH can foster humor better than either alone. We present CAHOOTS, an online chat system that suggests humorous images to its users to include in the conversation. We compare CAHOOTS to a regular chat system, and to a system that automatically inserts funny images using an artificial humor-bot. Users report that CAHOOTS made their conversations more enjoyable and funny, and helped them to express their personal senses of humor. Computer-Aided Humor offers an example of how systems can algorithmically augment human intelligence to create rich, creative experiences.

Introduction

Can a computer be funny? This question has intrigued the

pioneers of computer science, including Turing (1950) and

Minsky (1984). Thus far the answer seems to be, “No.”

While some computer errors are notoriously funny, the

problem of creating Computer-Generated Humor (CGH)

systems that intentionally make people laugh continues to

challenge the limits of artificial intelligence.

State-of-the-art CGH systems are generally textual. CHG

systems have tried to do everything from generating word-

play puns (Valitutti 2009) (e.g., “What do you get when

you cross a fragrance with an actor? A smell Gibson”) and

identifying contexts in which it would be funny to say,

“That’s what she said,” (Kiddon and Yuriy 2011) to

generating I-like-my-this-like-my-that jokes (Petrovic and

David 2013) (e.g., “I like my coffee like I like my war,

cold”) and combining pairs of headlines into tweets such as,

“NFL: Green Bay Packers vs. Bitcoin – live!”1 However,

none of these systems has demonstrated significant success.

Despite the challenge that computers face to automatically

generate humor, humor is pervasive when people use

computers. People use computers to share jokes, create

funny videos, and generate amusing memes. Humor and

1 http://www.twitter.com/TwoHeadlines

laughter have many benefits. Online, it fosters interpersonal

rapport and attraction (Morkes et al. 1999), and supports

solidarity, individualization and popularity (Baym 1995).

Spontaneous humor production is strongly related to

creativity, as both involve making non-obvious connections

between seemingly unrelated things (Kudrowitz 2010).

Computers and humans have different strengths, and

therefore their opportunity to contribute to humor differs.

Computers, for example, are good at searching large data

sets for potentially relevant items, making statistical

associations, and combining and modifying text and

images. Humans, on the other hand, excel at the complex

social and linguistic (or visual) processing on which humor

relies. Rather than pursuing humor solely through a CGH

strategy, we propose providing computational support for

humorous interactions between people using what we call

Computer-Aided Humor (CAH). We show that by allowing

the computer and human to work together, CAH systems

can help people be funny and express their own sense of

humor.

We explore the properties of this form of interaction and

prove its feasibility and value through CAHOOTS

(Computer-Aided Hoots), an online chat system that helps

people be funny (Figure 1). CAHOOTS supports ordinary

text chat, but also offers users suggestions of possibly funny

Figure 1. Images suggested by CAHOOTS in response to chat

line, “why u late?” (a), (b), and (e) are from image search query

“funny late”, (f) is from query “funny why”, (c) is a canned

reaction to questions, and (d) is a meme generated on-the-fly.

images to include based on the previous text and images in

the conversation. Users can select choices they find on-

topic or humorous and can add funny comments about their

choices, or choose not to include any of the suggestions.

The system was designed iteratively using paid crowd

workers from Amazon Mechanical Turk and interviews

with people who regularly use images in messaging.

We compare CAHOOTS to CGH using a chat-bot that

automatically inserts funny images, and to ordinary chat

with no computer humor. The bot uses the same images that

CAHOOTS would have offered as suggestions, but forcibly

inserts suggestions into the conversation. Compared to

these baselines, CAHOOTS chats were rated more fun, and

participants felt more involved, closer to one another, and

better able to express their sense of humor. CAHOOTS

chats were also rated as more fun than ordinary chat. Our

findings provide insights into how computers can facilitate

humor.

Related Work

In human-human interaction, humor serves several social

functions. It helps in regulating conversations, building

trust between partners and facilitating self-disclosure

(Wanzer et al. 1996). Non-offensive humor fosters rapport

and attraction between people in computer-mediated

communication (Morkes et al. 1999). It has been found that five percent of chats during work are intended to be primarily humorous (Handel and James 2002), and wall posts in Facebook are often used for sharing humorous content (Schwanda et al. 2012). Despite the popularity and benefits of humorous interaction, there is little research on how to support humor during computer-mediated communication. Instead, most related work focuses on computationally generating humor.

Computational Humor

Computational humor deals with automatic generation and

recognition of humor. Prior work has mostly focused on

recognizing or generating one specific kind of humor, e.g.

one-liners (Strapparava et al. 2011). While humorous

images are among the most prominent types of Internet-

based humor (Shifman 2007), little work addresses

computational visual humor.

Prior work on CGH systems focus on amusing individuals

(Dybala 2008; Valitutti et al. 2009). They find humor can

make user interfaces friendlier (Binsted 1995; Nijholt et al.

2003). Morkes et al. (1998) study how humor enhances

task-oriented dialogues in computer-mediated

communication. HumoristBot (Augello et al. 2008) can

both generate humorous sentences and recognize humoristic

expressions. Sjobergh and Araki (2009) designed a

humorous Japanese chat-bot. However, to the best of our

knowledge, no prior research has studied collaboratively

being funny using humans and computers.

Creativity Support Tools

CAH is a type of creativity support tool aimed specifically

at humor generation within online interaction. Shneiderman

(2007) distinguishes creativity support tools from

productivity support tools through three criteria: clarity of

task domain and requirements, clarity of success measures,

and nature of the user base.

Creativity support tools take many forms. Nakakoji (2006)

organizes the range of creativity support tools with three

metaphors: running shoes, dumbbells, and skis. Running

shoes improve the abilities of users to execute a creative

task they are already capable of. Dumbbells support users

learning about a domain to become capable without the tool

itself. Skis provide users with new experiences of creative

tasks that were previously impossible. For users who

already utilize image-based humor in their chats,

CAHOOTS functions as running shoes. For the remaining

users, CAHOOTS serves as skis.

System Design

Our system, CAHOOTS, was developed over the course of

many iterations. At the core of the system lie a number of

different algorithmic strategies for suggesting images.

Some of these are based on previous work, some are the

product of ideas brainstormed in discussions with

comedians and students who utilize images in messaging,

and others emerged from observations of actual system use.

Our system combines these suggestions using a simple

reinforcement learning algorithm for ranking, based on R-

Max (Brafman and Tennenholtz 2003), that learns weights

on strategies and individual images from the images chosen

in earlier conversations. This enabled us to combine a

number of strategies.

User Interface

CAHOOTS is embedded in a web-based chat platform

where two users can log in and chat with each other. Users

can type a message as they would in a traditional online

chat application, or choose one of our suggested humorous

images. Suggested images are displayed below the text

input box, and clicking on a suggestion inserts it into the

conversation. Both text and chosen images are displayed in

chat bubbles. See Figure 2 for an example. After one user

types text or selects an image, the other user is provided

with suggested image responses.

The Iterative Design Process

We initially focused on text-based humor suggestions based

on canned jokes and prior work (Valitutti et al. 2009).

These suffered from lack of context, as most human jokes

are produced within humorous frames and rarely

communicate meanings outside it (Dynel 2009). User

feedback was negative, e.g., “The jokes might be funny for

a three year old” and “The suggestions are very silly.”

Based on the success of adding a meme image into

suggestions, we shifted our focus to suggesting funny

images. In hindsight, image suggestions offer advantages

over text suggestions in CAHOOTS for multiple reasons:

images are often more open to interpretation than text;

images are slower for users to provide on their own than

entering text by keyboard; and images provide much more

context on their own, i.e., an image can encapsulate an

entire joke in a small space.

Image Suggestion Strategies

In this section, we describe our most successful strategies

for generating funny image suggestions based on context.

Emotional Reaction Images and gifs

Many chat clients provide emoticon libraries. Several

theories of computer-mediated communication suggest that

emoticons have capabilities in supporting nonverbal

communications (Walther and Kyle 2001). Emoticons are

often used to display or support humor (Tossell et al 2012).

In popular image sharing sites such as Tumblr2, users

respond to other people’s posts with emotional reaction

images or gifs. In CAHOOTS, we suggest reaction

images/gifs based on the emotion extracted from the last

sentence.

Previous work on sentiment analysis estimates the emotion

of an addresser from her/his utterance (Forbes-Riley and

Litman 2004). Recent work tries to predict the emotion of

the addressee (Hasegawa et al. 2013). Following this work,

we first use a lexicon-based sentiment analysis to predict

the emotion of the addresser. We adopt the widely used

NRC Emotion Lexicon3. We collect reaction images and

2 http://www.tumblr.com

3 http://saifmohammad.com/WebPages/lexicons.html

their corresponding emotion categories from reacticons.com.

We collect reaction gifs and their corresponding emotion

categories from reactinggifs.com. Then we suggest reaction

images and gifs based on one of five detected sentiments:

anger, disgust, joy, sadness, or surprise. An example of an

emotional reaction is shown in Figure 3.

Image Retrieval

We utilize image retrieval from Bing image4 search (Bing

image) and I Can Has Cheezburger5 (Cheezburger) to find

funny images on topic. Since Bing search provides a

keyword-based search API, we performed searches of the

form “funny keyword(s),” where we chose keyword(s)

based on the last three utterances as we found many of the

most relevant keywords were not present in the last

utterance alone. We considered both individual keywords

and combinations of words. For individual words, we used

the term frequency-inverse document frequency (tf-idf)

weighting, a numerical statistic reflecting how important a

word is to a document in a corpus, to select which

keywords to use in the query. To define tf-idf, let )

be 1 if term occurred in the th previous utterance Let

be the set of all prior utterances and write if term

was used in utterance . Then weighted tf and tf-idf are

defined as follows:

4 http://www.bing.com/images

5 http://icanhas.cheezburger.com

Figure 4. In response to the utterance, the user chooses a

suggestion generated by Bing image search with the query

"funny desert".

Figure 2. The CAHOOTS user interface in a chat, with user’s

messages (right in white) and partner's (left in blue). All text is

user-entered while images are suggested by the computer. The

system usually offers six suggestions.

Figure 3. In response to text with positive sentiment, we

suggest a positive emotional reaction image.

-

Here is the total number of utterances

collected during prototyping. The weights are designed to

prioritize words in more recent utterances. An example of a

single keyword for Bing is shown in Figure 4.

Combinations of keywords were also valuable. Humor

theorists argue humor is fundamentally based on

unexpected juxtaposition. The images retrieved with a

keyword combination may be funnier or more related to the

current conversation than images retrieved with a single

keyword. However, many word pairs were found to

produce poor image retrieval results. Consequently, we

compiled a list of common keywords, such as cat and dog,

which had sufficient online humorous content that they

often produced funny results in combination with other

words. If a user mentioned a common funny keyword, we

randomly pick an adjective or a noun to form a keyword

combination from the last three utterances. An example of a

query for a combination of keywords is shown in Figure 5.

Memes Meme images are popular forms of Internet humor.

Coleman (2012) defines online memes as, “viral images,

videos, and catchphrases under constant modification by

users”. A “successful” meme is generally perceived as

humorous or entertaining to audiences.

Inspired by internet users who generate their own memes

pictures through meme generation website and then use

them in conversations in social media sites like Reddit or

Imgur, our meme generation strategy writes the last

utterance on the top and bottom of a popular meme

template. A meme template is an image of a meme

character without the captions. The template is chosen

using a machine-learning trained classifier to pick the most

suitable meme template image based on the last utterance,

as in Figure 1(d), with half of the text on the top and half on

the bottom. To train our classifier to that match text

messages to meme template, we collected training instances

from the Meme Generator website6

. This website has

tremendous numbers of user-generated memes consisting of

text on templates. In order to construct a dataset for training

machine learning models, we collected the most popular

one hundred meme templates and user generated meme

instances from that site. To filter out the memes that the

users find personally humorous, we only keep those memes

with fifty or more “upvotes” (N = 7,419). We use LibLinear

(Fan et al. 2008), a machine learning toolkit, to build a one-

vs-the-rest SVM multi-class classifier (Keerthi et al. 2008)

based upon Bag-of-words features. Even though this is

multi-class classification with one hundred classes, the

classifier trained in this simple way achieved 53% accuracy

(compared with a majority-class baseline of 9%).

The fact that the meme's text often matched exactly what

the user had just typed often surprised a user and led them

to ask, “are you a bot?” Also note that we have other

strategies for generating different types of image memes,

which modify the text, such as the Doge meme illustrated in

Figure 6.

Canned Responses

For certain common situations, we offer pre-selected types

of funny images. For example, many users are suspicious

that they are actually matched with a computer instead of a

real person (which is partly accurate). As mentioned, we

see users asking their partner if he/she is a bot. As a canned

response, we suggest the results of keyword-search for

“funny dog computer,” “funny animal computer,” or “funny

CAPTCHA”.

Responding to Images with Images

We observed users often responding to images with similar

images. For example, a picture of a dog would more likely

be chosen as a response to a picture of a dog. Hence, the

respond-in-kind strategy responds to an image chosen from

a search for “funny keyword” with a second image from the

same search, for any keyword.

Another strategy, called the rule-of-three, will be triggered

after a user selects a respond-in-kind. The rule-of-three will

perform an image search for “many keyword” or “not

6 http://memegenerator.net

Figure 6. A “Doge” meme example.

Figure 5. An example of an utterance that generated a

keyword combination cat gerbil, and a resulting image

retrieved for the search funny cat gerbil.

keyword”. An example is shown in Figure 7. The rule-of-

three is motivated by the classic comic triple, a common

joke structure in humor (Quijano 2012). Comedians use the

first two points to establish a pattern, and exploit the way

people's minds perceive expected patterns to throw the

audience off track (and make them laugh) with the third

element. In our system, when the last two images are both

Bing image retrieved with the same keyword, e.g. funny

dog images, we will suggest a Bing funny image with

“many”+ keyword (e.g. “many dog”) or “no” + keyword

(e.g. “no dog”) image as the third element.

In response to images, “LOL”, “amused” or “not-amused”

reaction images and gifs were suggested to help users

express their appreciation of humor.

Ranking Suggestions using Reinforcement Learning

The problem of choosing images to select fits neatly into

the paradigm of Reinforcement Learning (RL). Our RL

algorithm, inspired by R-Max (Brafman and Tennenholtz

2003), maintains counts at three levels of specificity, for

number of times a suggestion was offered and number of

times it was accepted. The most general level of counts is

for each of our overall strategies. Second, for specific

keywords, such as “dog,” we count how many times, in

general, users are offered and choose an image for a query

such as “funny dog.” Finally, for some strategies, we have a

third level of specific counts, such as a pair for each of the

fifty images we receive from Bing’s API. We use the

“optimistic” R-Max approach of initializing count pairs as

if each had been suggested and chosen five out of five

times. The score of a suggestion is made based on a back-

off model, e.g., for a Bing query “funny desert”: if we have

already suggested a particular image multiple times, we will

use the count data for that particular image, otherwise if we

have sufficient data for that particular word we will use the

count data for that word, and otherwise we will appeal to

the count data we have for the general Bing query strategy.

Experiments

To test the feasibility of CAH we performed a controlled

study. Before the experiment began, we froze the

parameters in the system and stopped reinforcement

learning and adaptation.

Methodology

Participants were paid Mechanical Turk workers in the

United States. Each pair of Turkers chatted for 10 minutes

using: 1) CAHOOTS, our CGH system, 2) plain chat (no

image suggestions), or 3) a CGH system with computer-

generated images, all using the same interface In the CGH

system, whenever one user sends out a message, our system

automatically inserted the single top-ranking funny image

suggestion into the chat, with “computer:” inserted above

the message, as is common in systems such as WhatsApp.

Assignment to system was based on random assignment.

We also varied the number of suggestions in CAHOOTS.

We write CAHn to denote CAHOOTS with n suggestions.

We use CAHOOTS and CAH6 interchangeably (6 was the

default number determined in pilot studies). The systems

experimented with were CGH, plain chat, CAH1, CAH2,

CAH3, CAH6, CAH10.

A total of 738 participants (408 male) used one of systems,

with at least 100 participants using each variant. Pairs of

participants were instructed how to use the system and

asked to converse for at least 10 minutes. After the chat,

participants were asked to fill out a survey to evaluate the

conversation and the system. We asked participants to what

extent they agree with four statements (based on Jiang et al.

2011), on a 7 point Likert scale. The four statements were:

The conversation was fun.

I was able to express my sense of humor in this

conversation.

I felt pretty close to my partner during the

conversation.

I was involved in the conversation.

Experiments

Averaged over the chats where our system made

suggestions (CAH1,2,3,6,10) participants selected an image in

31% of the turns. In contrast, a field study found emoticons

to be used in 4% of text messages (Tossell et al. 2012).

System Variant

Figure 8 summarizes participants’ responses for the four

Likert questions. Results are shown for chat, CGH, and two

variants of CAHOOTS. P-values were computed using a

one-sided Mann-Whitney U test.

Figure 7. The “rule of three” strategy suggests putting an

image of many dogs after two dog images.

CAHOOTS vs. CGH

Participants rated CAHOOTS conversations better on

average than CGH with p-values less than 0.05 for all four

questions -- more fun, able to express sense of humor,

closer to partner, and more involved in conversation

It is also interesting to compare CAH1 to CGH as this

reflects the difference between one image automatically

into the conversation and one image offered as a

suggestion. Here CAH1 got higher response for fun,

involvement, and closeness than CGH again with p < .05.

Curiously, participants using CAH1 felt somewhat less able

to express their senses of humor.

CAHOOTS vs. plain chat

CAHOOTS was also rated more fun than plain chat (p <

.05), and CAHOOTS participants also reported being able

to express their own sense of humor better than plain chat

participants (p < .05). For the other two questions

CAHOOTS was not statistically significantly better than

plain chat.

Note that while it may seem trivial to improve on plain chat

by merely offering suggestions, our earlier prototypes

(especially with text but even some with image suggestions)

were not better than plain chat.

Number of Suggestions

Figure 9 shows responses to the fun question for different

numbers of suggestions in CAHOOTS. In general, more

suggestions makes the conversation more fun, though ten

suggestions seemed to be too many. This may be because of

the cognitive load required to examine ten suggestions or

simply that with many suggestions scrolling is more likely

to be required to see all image suggestions.

Effective Image Generation Strategies

As described earlier we used several different strategies for

generating images. Table 1 shows how often each type was

shown and how often it was selected. The rule-of-three

(inspired by our meetings with comedians) was suggested

less often than some other techniques, but the rate at which

it was selected was higher. Reaction images/gifs were the

next most frequently selected image strategy.

# suggestions % chosen

Bing Images 44,710 10%

Reaction Images and gifs 4,375 19%

Meme 709 13%

Rule-of-three 698 24%

Cheezburger 537 7%

Table 1 Selection rate of the top five strategies.

Limitations

Since we evaluate our system with paid workers, we have

only tested the system between anonymous strangers whose

only commonality is that they are US-based Mechanical

Turk workers. We also asked workers to indicate with

whom they would most like to use CAHOOTS: a family

member, a close friend, an acquaintance, a colleague, or a

stranger. Workers consistently answer that CAHOOTS

would be best when chatting with a close friend who “can

understand their humor.”

Also, we cannot compare CAHOOTS to every kind of

CGH. For example, it is possible that users would prefer a

CGH system that interjects images only once in a few turns

or only when it is sufficiently confident.

Qualitative Insights

We analyzed the content of the text and image messages as

well as worker feedback from both prototyping and

experimentation phases. Note participants often remarked

to one another, quite candidly, about what they liked or

problems with our system, which helped us improve.

Anecdotally, feedback was quite positive, e.g., “It should be

used for online speed dating!” and “When will this app be

available for phones and whatnot? I want to use it!” Also,

note that when we offered a small number of suggestions,

feedback called for more suggestions. In contrast, feedback

for CGH was quite negative, such as “The pictures got kind

Figure 8. Mean Likert ratings with Standard Error. 7 is

strongly agree, 1 is strongly disagree, and the statements

were 1. The conversation was fun. 2. I was able to express

my sense of humor in this conversation. 3. I felt pretty close

to my partner during the conversation. 4. I was involved in

the conversation.

3.5

4.5

5.5

6.5

fun express humor

felt close involved

Plain chat CGH CAH_1 CAHOOTS

Figure 9. Mean and SE for "the conversation was fun" as we

vary the number of suggestions, with 0 being plain chat.

5

5.2

5.4

5.6

5.8

6

0 2 4 6 8 10

Ho

w f

un

Number of suggestions

of distracting while I was trying to talk to him/her.” We

now qualitatively summarize the interactions and feedback.

Humorous Images Bring New Topics to the Conversation

Without CAHOOTS image suggestions, most of the chats

focused on working in Mechanical Turk, which they

seemed to find interesting to talk about. With suggestions,

however, workers chose an image that suited their interests

and naturally started a conversation around that image.

Common topics included their own pets after seeing funny

animal images, and their own children and family, after

seeing funny baby images. As one worker commented:

“great for chatting with a stranger, starts the conversation.”

An example is shown in Figure 10, where two workers start

to talk about Bill Murray after using a reaction gif featuring

Bill Murray.

Image Humor is Robust

We found CAHOOTS robust in multiple ways. First,

participants had different backgrounds which made them

understand images differently. For example, one participant

might complain that our memes were outdated, while the

other participant’s feedback would indicate that they didn’t

even recognize that the images were memes in the first

place. Nonetheless, the latter could still find the images

amusing even if they didn’t share the same background.

Second, we found CAHOOTS robust to problems that

normal search engines face. For example, a normal search

engine might suffer from ambiguity and therefore perform

word-sense disambiguation, whereas humor is often

heightened by ambiguity and double-entendres. While we

didn’t explicitly program in word-sense ambiguity, it often

occurs naturally.

Yes, and…

A common rule in improvisational comedy, called the yes

and rule, is that shows tend to be funnier when actors

accept one another’s suggestions and try to build them into

something even funnier, rather than changing the direction

even if they think they have a better idea (Moshavi 2001).

Many CAHOOTS’s strategies lead to yes-and behaviors.

An example is shown in Figure 11. On the top, the

computer suggestions directly addresses the human’s

remark to makes the conversation funnier.

Users Tend to Respond with Similar Images

Humor support, or the reaction to humor, is an important

aspect of interpersonal interaction (Hay 2001). With

CAHOOTS, we find that users tended to respond to a funny

image with a similar image to contribute more humor, show

their understanding and appreciation of humor. When one

user replied to her partner’s image message with an image,

35% of the time the other user chose an image generated by

the same strategy. Compared with two random images in a

conversation, the chance that they are generated by the

same strategy is 22%.

Conclusion

In this paper we introduce the concept of Computer-Aided

Humor, and describe CAHOOTS—a chat system that

builds on the relative strengths of people and computers to

generate humor by suggesting images. Compared to plain

chat and a fully-automated CGH system, people using

found it more fun, enabled them to express their sense of

humor and more involvement.

The interaction between human and computer and their

ability to riff off one another creates interesting synergies

and fun conversations. What CAHOOTS demonstrates is

that the current artificial intelligence limitations associated

with computational humor may be sidestepped by an

interface that naturally involves humans. A possible

application of CAH would be an add-on to existing chat

clients or Facebook/Twitter comment box that helps

individuals incorporate funny images in computer-mediated

communication.

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